Yinyin Yuan
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View article: Histopathology-based Spatial Profiling of Immune and Molecular Features Predicts Cancer risk in Barrett’s Esophagus
Histopathology-based Spatial Profiling of Immune and Molecular Features Predicts Cancer risk in Barrett’s Esophagus Open
Background Improved cancer risk stratification is needed to differentiate high-risk individuals with Barrett’s esophagus (BE) from low-risk populations to reduce overtreatment and improve outcome. The evolution of BE towards adenocarcinoma…
View article: Oral Doxorubicin Lipid Nanoparticles Enhance Triple‐Negative Breast Cancer Therapy via Immune Activation and Reduced Cardiotoxicity
Oral Doxorubicin Lipid Nanoparticles Enhance Triple‐Negative Breast Cancer Therapy via Immune Activation and Reduced Cardiotoxicity Open
Doxorubicin (Dox) is a cornerstone chemotherapeutic agent for treating triple‐negative breast cancer, but its clinical utility is limited by cardiotoxicity. While oral administration can circumvent the toxicity risks of intravenous deliver…
View article: Dysregulated <i>SASS6</i> expression promotes increased ciliogenesis and cell invasion phenotypes
Dysregulated <i>SASS6</i> expression promotes increased ciliogenesis and cell invasion phenotypes Open
Centriole and/or cilium defects are characteristic of cancer cells and have been linked to cancer cell invasion. However, the mechanistic bases of this regulation remain incompletely understood. Spindle assembly abnormal protein 6 homolog …
View article: Identification and validation of extracellular matrix-related genes in the progression of gastric cancer with intestinal metaplasia
Identification and validation of extracellular matrix-related genes in the progression of gastric cancer with intestinal metaplasia Open
BACKGROUND Gastric cancer (GC) is a highly lethal malignancy with a high incidence and mortality rate globally. Its development follows the Correa model, with intestinal metaplasia (IM) being a critical precursor to GC. However, the mechan…
View article: TMIC-68. DEEP LEARNING OF THE SPATIAL IMMUNE ENGAGEMENT IN GLIOBLASTOMA UNDER TEMOZOLOMIDE TREATMENT
TMIC-68. DEEP LEARNING OF THE SPATIAL IMMUNE ENGAGEMENT IN GLIOBLASTOMA UNDER TEMOZOLOMIDE TREATMENT Open
Effective immune synapses are a prerequisite for desirable immune surveillance and control of tumour growth. Cell-to-cell interactions form the basis of immune synapses, resulting in either immunoprotected or immunosuppressed manifestation…
View article: Supplementary Data from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma
Supplementary Data from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma Open
Table S3 and Fig. S1-S10
View article: Data from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma
Data from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma Open
Beyond tertiary lymphoid structures, a significant number of immune rich areas without germinal center-like structures are observed in non-small cell lung cancer. Here, we integrated transcriptomic data and digital pathology images to stud…
View article: Table S1 from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma
Table S1 from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma Open
Differentially expressed genes in TCGA LUSC and LUAD patients with high versus low S_intra/immune
View article: Supplementary Data from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma
Supplementary Data from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma Open
Table S3 and Fig. S1-S10
View article: Table S2 from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma
Table S2 from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma Open
Lists of TCGA LUSC and LUAD cases in the discovery and validation cohort
View article: Table S1 from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma
Table S1 from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma Open
Differentially expressed genes in TCGA LUSC and LUAD patients with high versus low S_intra/immune
View article: Table S2 from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma
Table S2 from Spatial positioning of immune hotspots reflects the interplay between B and T cells in lung squamous cell carcinoma Open
Lists of TCGA LUSC and LUAD cases in the discovery and validation cohort
View article: Supplementary Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Supplementary Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Supplementary methods, tables and figures.
View article: Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphologic, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissect…
View article: Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphologic, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissect…
View article: Table 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Table 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Patient characteristics: MGUS.
View article: Figure 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Figure 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Computational methods for bone thickness analysis and cell infiltration patterns: A, Image analysis to estimate bone thickness (Supplementary Materials and Methods). Using the same BM sample image as Fig. 1A, the bone segmentation (ii) is …
View article: Figure 3 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Figure 3 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Performance evaluation of MoSaicNet and AwareNet deep learning models: A, The ROC curves and AUC values of the MoSaicNet superpixel classifier. The values in brackets indicate the 95% CI. B, Two-dimensional mapping of superpixels using MoS…
View article: Supplementary Table 10 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Supplementary Table 10 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Supplementary Table 10
View article: Figure 5 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Figure 5 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Density of immune T cells and plasma cells in MGUS, NDMM, and posttreatment samples. A–G, Box plots showing the difference in density of FOXP3+CD4+ (A), the density of CD8+ (B), the density of FOXP3−CD4+ (C), FOXP3+CD4+:FOXP3−CD4+ ratio (D…
View article: Table 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Table 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Patient characteristics: paired diagnostic and posttreatment samples.
View article: Figure 4 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Figure 4 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Studying bone physiology using MoSaicNet. A, Proportion of different compartments of BM trephine digital images. B–E, One stacked bar represents a sample. Box plots showing the difference in %bone between samples from NDMM and posttreatmen…
View article: Figure 6 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Figure 6 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
A and B, Spatial neighborhood of immune and tumor cells (A and B) and between MGUS and NDMM (B). The P* indicate P values after multiple testing correction using the BH method. The points represent the mean and the bars are 95% CIs, indica…
View article: Supplementary Table 10 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Supplementary Table 10 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Supplementary Table 10
View article: Supplementary Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Supplementary Data from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Supplementary methods, tables and figures.
View article: Figure 6 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Figure 6 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
A and B, Spatial neighborhood of immune and tumor cells (A and B) and between MGUS and NDMM (B). The P* indicate P values after multiple testing correction using the BH method. The points represent the mean and the bars are 95% CIs, indica…
View article: Figure 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Figure 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Overview of computational deep learning and image processing pipelines for BM MIHC images: A, MoSaicNet pipeline. The polygons (black) indicate superpixels. MoSaicNet dissects a tissue section into bone, blood, fat, and cellular tissue reg…
View article: Figure 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies
Figure 1 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies Open
Overview of computational deep learning and image processing pipelines for BM MIHC images: A, MoSaicNet pipeline. The polygons (black) indicate superpixels. MoSaicNet dissects a tissue section into bone, blood, fat, and cellular tissue reg…